The present disclosure relates generally to driver workload estimation and in particular, to an adaptive driver workload estimator.
Driver workload estimator (DWE) refers to real-time continuous estimation of a driver's workload index by monitoring the driver, the vehicle and the driving environment. A DWE performs workload assessment continuously in real-time, under naturalistic driving conditions, and in a way that is highly unobtrusive to drivers. In some cases, when driver workload information is available, a number of in-vehicle functionalities can be enhanced, for example: presentation of information to drivers, active safety driver assistance, and vehicle chassis control.
There are three major types of driver workload: visual, manual and cognitive. When a driver is stressed by the manual driving task, or when the driver is mentally involved in a secondary task while driving, such as engaging in a cellular telephone conversation, his or her cognitive workload becomes high. The capability of detecting hazards and handling an elicited problem may be reduced. Cognitive workload is the most difficult to measure among the three major types of workload; it is essentially internal to the driver and only partially observable.
A long list of parameters is believed to be capable of reflecting a driver workload. The parameters include items such as: lane position deviation, lane departure duration, speed deviation, steering hold, brake pressure, vehicle headway, driver eye gaze fixation duration, eye gaze position variance, heart-beat interval variance, etc. While some are closer reflections of a driver's cognitive internal state than others, none alone stands to be a reliable indicator. Research has shown that the fusion of the parameters tends to provide better overall performance than individual parameters. Currently, there is no driver workload system that performs this fusion to provide a driver workload estimate.
The current DWEs are built with a manual workload analysis and modeling process, in which only parameters that demonstrate a high driver workload correlation are selected and manually modeled. These DWEs may omit promising candidate parameters that do not follow a unimodal Gaussian distribution, which is assumed by the design method. A parameter showing low correlation with workload level under the unimodal assumption is not necessarily a bad workload indicator.
In addition, the current “handcrafted” DWE models tend to be simple, such as linear regression models. Their parameter coverage is generally limited, such as the binary heuristics based workload estimators. This low level of model sophistication may be due to the cumbersome manual workload analysis and modeling method. As the number and capabilities of sensors increase in vehicles, and the list of promising parameters grows, these DWEs may not be able to adapt structurally. In order to account for these changes, estimation algorithms in current DWEs may have to be redesigned. Further, the current DWEs tend to be static, in that their estimation algorithms are “cemented” based on one set of available model data. They are not tailored and adaptive to individual driver workload characteristics.